These examples explain machine learning models applied to tabular data. They are all generated from Jupyter notebooks available on GitHub.
Examples demonstrating how to explain tree-based machine learning models.
- Basic SHAP Interaction Value Example in XGBoost
- Catboost tutorial
- Census income classification with LightGBM
- Census income classification with XGBoost
- Example of loading a custom tree model into SHAP
- Explaining a simple OR function
- Explaining the Loss of a Tree Model
- Fitting a Linear Simulation with XGBoost
- Force Plot Colors
- Front page example (XGBoost)
- League of Legends Win Prediction with XGBoost
- NHANES I Survival Model
- Speed comparison of gradient boosting libraries for shap values calculations
- Python Version of Tree SHAP
- Scatter Density vs. Violin Plot
- Understanding Tree SHAP for Simple Models
Examples demonstrating how to explain linear machine learning models.
Examples demonstrating how to explain machine learning models based on neural networks.
Examples demonstrating how to explain arbitrary machine learning pipelines.
- Census income classification with scikit-learn
- Diabetes regression with scikit-learn
- Iris classification with scikit-learn
- SHAP Values for Multi-Output Regression Models
- Create Multi-Output Regression Model
- Get SHAP Values and Plots
- Simple Boston Demo
- Simple Kernel SHAP
- How a squashing function can effect feature importance